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Hypothesis testing with nonlinear shape models.

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Dept. of Computer Science, Univ. of North Carolina, Chapel Hill, NC 27599, USA.


We present a method for two-sample hypothesis testing for statistical shape analysis using nonlinear shape models. Our approach uses a true multivariate permutation test that is invariant to the scale of different model parameters and that explicitly accounts for the dependencies between variables. We apply our method to m-rep models of the lateral ventricles to examine the amount of shape variability in twins with different degrees of genetic similarity.

[Indexed for MEDLINE]

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